...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Image Processing, IET >Medical image fusion using the PCNN based on IQPSO in NSST domain
【24h】

Medical image fusion using the PCNN based on IQPSO in NSST domain

机译：基于IQPSO的PCNN在NSST域中使用PCNN的医学图像融合

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this study, an improved quantum-behaved particle swarm optimisation based pulse-coupled neural network (IQPSO-PCNN) is proposed in the non-subsampled shearlet transform (NSST) domain for medical image fusion. First, NSST tool is used to decompose the source image into low-frequency and high-frequency subbands. Then, for low-frequency subbands, the fusion rules of two different functions are presented, which simultaneously addresses two key issues of energy preservation and detail extraction. For high-frequency subbands, unlike conventional PCNN-based methods, parameters are manually set based on experience, and the decomposed high-frequency subbands share a set of parameters. The IQPSO-PCNN model can obtain the optimal parameters for each high-frequency subband adaptively according to its own information. Finally, the fused low-frequency subband and high-frequency subbands are inversely transformed by NSST to acquire the final fused image. The proposed algorithm uses >90 pairs of images with four different modalities. In addition, fusion experiments are performed on different sequences of the three modes. The experimental results demonstrate that the proposed method is superior to existing state-of-art methods in subjective visual performance and objective evaluation.
机译：在该研究中，提出了一种基于量子表现粒子群的脉冲耦合神经网络（IQPSO-PCNN），用于医学图像融合的非已撤销的Shearlet变换（NSST）域。首先，NSST工具用于将源图像分解为低频和高频子带。然后，对于低频子带，呈现了两个不同功能的融合规则，其同时解决了能量保存和细节提取的两个关键问题。对于高频子带，与传统的基于PCNN的方法不同，基于经验手动设置参数，并且分解的高频子带共享一组参数。 IQPSO-PCNN模型可以根据其自身信息自适应地获得每个高频子带的最佳参数。最后，熔融的低频子带和高频子带由NSST反转以获取最终融合图像。所提出的算法使用> 90对图像，具有四种不同的方式。另外，在三种模式的不同序列上进行融合实验。实验结果表明，该方法优于现有的主观视觉性能和客观评估。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号